Sanat Thukral

AI and ML advancements have led to models for learning complex patterns in EEG data and brain activation [3,7]. These models contribute to understanding human behavior and brain responses, motivating the design of passive brain-computer interfaces (pBCIs) for creating novel adaptive and personalized technologies. pBCIs enhance human-computer interactions by facilitating communication channels between the brain and external devices [5]. A critical use case is estimating mental workload (MWL) to develop user-centered applications, with EEG data often used [1],[2]. Unsupervised autoencoders can learn complex patterns in EEG data, but they are black-boxes, making interpretation and explanation difficult. This research aims to create explainable models for MWL estimation using EEG data in E-learning [3]. The model will offer insights into cognitive processes and mental workload variations experienced by students on an E-learning platform [3]. Variations in taskload will be induced by manipulating the perceptual modality from Multiple Resource Theory [8] in the E-learning interface, expecting different mental workload levels. In summary, this study contributes to the body of knowledge by developing a real-time MWL model induced from EEG data for E-learning contexts. This model will underpin the construction of pBCIs that facilitate the design of human-centered technologies aligned with brain information processing.

Works Cited: 1. Longo, L. (2022). Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning. *Brain Sciences*, *12*(10), 1416.

2. Raufi, B., & Longo, L. (2022). An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload. *Frontiers in Neuroinformatics*, *16*, 44.

3. Das, A., & Rad, P. (2020). Opportunities and challenges in explainable artificial intelligence (xai): A survey. *arXiv preprint arXiv:2006.11371*. https://arxiv.org/pdf/2006.11371.pdf

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5. Hancock, G. m., Longo, L., Young, M. s., & Hancock, P. a. (2021). Mental Workload. In HANDBOOK OF HUMAN FACTORS AND ERGONOMICS (pp. 203–226). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119636113.ch7

6. Longo, L. (2014). Formalising Human Mental Workload as a Defeasible Computational Concept [Thesis, Trinity College Dublin]. http://www.tara.tcd.ie/handle/2262/72197

7. Moustafa, K., Luz, S., & Longo, L. (2017). Assessment of Mental Workload: A Comparison of Machine Learning Methods and Subjective Assessment Techniques. In L. Longo & M. C. Leva (Eds.), Human Mental Workload: Models and Applications (pp. 30–50). Springer International Publishing. https://doi.org/10.1007/978-3-319-61061-0_3

8. Wickens, C. D. (2008). Multiple Resources and Mental Workload. *Human Factors*, *50*(3), 449–455. https://doi.org/10.1518/001872008X288394